Explore the emergence of Scala as an unexpected lingua franca for data science in this conference talk from Scala Days Berlin 2016. Delve into the factors driving Scala's adoption among data scientists, including the rise of distributed technologies and the JVM platform. Discover how Apache Spark and other Scala-based tools are transforming data analysis workflows. Learn about new methodologies and scientific advances in machine learning implemented in Scala, and see demonstrations of interactive, reactive, and robust data science environments using Spark Notebook and Docker. Gain insights into Scala's functional programming features, type safety, and domain-specific capabilities that make it well-suited for data manipulation and analysis. Compare Scala's advantages to legacy languages like Python and R, and understand the challenges and opportunities in adopting Scala for data science projects. Witness practical examples of Scala in action, including data visualization, geospatial analysis, time series processing, and integration with Spark streaming.
Overview
Syllabus
Introduction
Agenda
Why Scala
Spark Notebook
Markdown
Scala Features
Functional Programming
Spark
Python
Tuples
Pattern Matching
Case Classes
Type Inference
Type Safety
Unification
DomainSpecific
Notebooks
Spark Sessions
Caching
Sequel Query
Benefits of Scala
Scala Disadvantages
Teasers for tomorrow
We are still lacking
tooling and models
Spy Notebook
Spark Context
Notebook Driver
Notebook Stability
Notebook Objects
Legacy Objects
Reproducibility
Spark Connector
Metadata
Logs
Side Panel
Plotting
Graph DSL
GeoPoint Shot
Spatial Information
Reshape Data
Parse Data
Create a Dual Chart
Create a Rudder
Parallel coordinates
Time series
Time service
Reactive components
Synchronization
Models Algorithms
Spark Streaming
Other libraries
Docker
Courses
Taught by
Scala Days Conferences
